A deep neural network (DNN) is a computational machine learning method. DNNs belong to a class of artificial neural networks (NN). With NNs, a computational graph is constructed which imitates the features of a biological neural network. The biological neural network includes features salient for computation and responsible for many of the capabilities of a biological system that may otherwise be difficult to capture through other methods. In some implementations, such networks are arranged into a sequential layered structure in which connections are unidirectional. For example, outputs of artificial neurons of a particular layer can be connected to inputs of artificial neurons of a subsequent layer. A DNN can be a NN with a large number of layers (e.g., 10s, 100s, or more layers).
Different NNs are different from one another in different perspectives. For example, the topologies or architectures (e.g., the number of layers and how the layers are interconnected) and the weights of different NNs can be different. A weight can be approximately analogous to the synaptic strength of a neural connection in a biological system. Weights affect the strength of effect propagated from one layer to another. The output of an artificial neuron can be a nonlinear function of the weighted sum of its inputs. A NN can be trained on training data and then used to determine an output from untrained data.